A mathematical model of the action
of a broadly tuned chemical sensor array has been developed explaining
the performance of such systems to large numbers of individual stimulus
components. A chapter in the Handbook
of Machine Olfaction provides the details [pdf]

Research

Main scientific goals:

To understand the neuronal information processing
underpinning olfaction (smell) and use this to build better machines to sense
molecular stimuli (machine olfaction). How building models of sensory information
processing helps us better understand the nervous system. This work combines
both practical and theoretical approaches:

Practical:

This work involves developing neuronal models
of the olfactory pathway that are driven by real-world chemosensors as a test-bed
for biologically inspired signal processing architectures. I have worked with
conducting polymer, optical, and metal oxide semiconductor chemosensor devices.

Theoretical:

The olfactory systems relies upon a large
repetoire of different chemoreceptor types in order to encode different molecular
stimuli. The signals can be considered to act as a population code representation

.
Recent work has included considering this population coding as a

geometric
transformation and using Fisher Information to quanitify the accuracy of stimulus
estimation. These concepts are being investigated for optimising chemical sensor
arrays for practical applications